평범한 필기장

https://arxiv.org/abs/2209.14988 DreamFusion: Text-to-3D using 2D DiffusionRecent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoiarxiv.org1. Introduction Diffusion model은 다양한 다른 modality에서 적용되는데 성..

https://arxiv.org/abs/2303.12789 Instruct-NeRF2NeRF: Editing 3D Scenes with InstructionsWe propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images whiarxiv.org1. Introduction NeRF와 같은 3D reconstruction 기술..

https://arxiv.org/abs/2303.11989 Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image ModelsWe present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input. To this end, we leverage pre-trained 2D text-to-image models to synthesize a sequence of images from different poses. In order to lift these outparxiv.org요약다루는 task : 2D Text-to-Image m..

https://arxiv.org/abs/2308.04079 3D Gaussian Splatting for Real-Time Radiance Field RenderingRadiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methoarxiv.org1. Introduction NeRF 기반의 방식들은 high quali..

https://arxiv.org/abs/2003.08934 NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisWe present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-conarxiv.org 랩실 인턴을 시작하기 전에 연구 주제에 대한 레퍼런스..

https://arxiv.org/abs/2405.00878 SonicDiffusion: Audio-Driven Image Generation and Editing with Pretrained Diffusion ModelsWe are witnessing a revolution in conditional image synthesis with the recent success of large scale text-to-image generation methods. This success also opens up new opportunities in controlling the generation and editing process using multi-modal input. Warxiv.org1. Introdu..

https://arxiv.org/abs/2312.06738 InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction FollowingThe ability to provide fine-grained control for generating and editing visual imagery has profound implications for computer vision and its applications. Previous works have explored extending controllability in two directions: instruction tuning with textarxiv.orghttps://github.com/jack..

https://arxiv.org/abs/2112.10741 GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsDiffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and carxiv.org1. Intro..

https://arxiv.org/abs/2404.09512 Magic Clothing: Controllable Garment-Driven Image SynthesisWe propose Magic Clothing, a latent diffusion model (LDM)-based network architecture for an unexplored garment-driven image synthesis task. Aiming at generating customized characters wearing the target garments with diverse text prompts, the image controllarxiv.org1. Introduction 본 논문의 주 contribution을 요약하..

1. Introduction 본 논문은 Imagen을 도입하는데 이는 text-to-image 합성에서 전례없는 정도의 photorealism과 깊은 수준의 언어 이해를 가져오기 위해 transformer language models와 high-fidelity diffusion model을 결합한 text-to-image diffusion model이다. Imagen의 key finding은 text-only corpora로 기학습된 large LM으로부터 text embedding이 text-to-image 합성에서 놀라운 효과적이라는 것이다. Imagen은 input text를 sequence of embeddings로 매핑하기 위한 frozen T5-XXL encoder와 $64 \times 64$..